Using GPS-data to determine optimum electric vehicle ranges: A Michigan case study

Fuel-switching personal transportation from gasoline to electricity offers many advantages, including lower noise, zero local air pollution, and petroleum-independence. But alleviations of greenhouse gas (GHG) emissions are more nuanced, due to many factors, including the car’s battery range. The authors use GPS-based trip data to determine use type-specific, GHG-optimized ranges. The dataset comprises 412 cars and 384,869 individual trips in Ann Arbor, Michigan, USA. The authors use previously developed algorithms to determine driver types, such as using the car to commute or not. Calibrating an existing life cycle GHG model to a forecast, low-carbon grid for Ann Arbor, the authors find that the optimum range varies not only with the drive train architecture (plugin-hybrid versus battery-only) and charging technology (fast versus slow) but also with the driver type. Across the 108 scenarios the authors investigated, the range that yields lowest GHG varies from 65 km (55+ year old drivers, ultrafast charging, plugin-hybrid) to 158 km (16–34 year old drivers, overnight charging, battery-only). The optimum GHG reduction that electric cars offer – here conservatively measured versus gasoline-only hybrid cars – is fairly stable, between 29% (16–34 year old drivers, overnight charging, battery-only) and 46% (commuters, ultrafast charging, plugin-hybrid). The electrification of total distances is between 66% and 86%. However, if cars do not have the optimum range, these metrics drop substantially. The authors conclude that matching the range to drivers’ typical trip distances, charging technology, and drivetrain is a crucial pre-requisite for electric vehicles to achieve their highest potential to reduce GHG emissions in personal transportation.

• Record URL:
  https://doi.org/10.1016/j.trd.2019.102203
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S1361920919301191
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/13619209
• Supplemental Notes:
  • © 2019 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Meinrenken, Christoph J
  • Shou, Zhenyu
  • Di, Xuan
• Publication Date: 2020-1

Language

• English

Media Info

• Media Type: Web
• Features: Glossary; References; Tables;
• Serial:
  • Transportation Research Part D: Transport and Environment
  • Volume: 78
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1361-9209
  • Serial URL: http://www.sciencedirect.com/science/journal/13619209

Subject/Index Terms

• TRT Terms: Case studies; Electric vehicles; Greenhouse gases; Life cycle analysis; Optimization; Trip length; Vehicle range
• Geographic Terms: Michigan
• Subject Areas: Energy; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01728096
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 28 2020 9:42AM